Staff Software Engineer, Discovery Platform

Whatnot • Commerce • Marketplaces • 7 days ago
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Salary
$207k – $230k per year
Location
In office (San Francisco)
Seniority
Staff
Visa
H-1B filings in 12 months: 48 · for this role: 31 · green card filings: 8
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 16, 2026. First seen by Alion on Jun 30, 2026. Whatnot scores C on the Alion truth index.

Overview
Company
Impact
Profile match

Whatnot

Whatnot is a US live shopping marketplace headquartered in San Francisco where sellers host livestreams to sell trading cards, collectibles, fashion, electronics, live plants and many other categories. Founded in 2019 and backed by investors including Andreessen Horowitz, Y Combinator and CapitalG, it operates in North America, Europe, Australia and Japan with teams in New York, Los Angeles, London, Dublin, Berlin, Krakow, Sydney and Tokyo. It hires software and application security engineers, product designers, sales managers for international markets, recruiters, communications managers and finance and tax staff.
whatnot.com • HQ: San Francisco, United States • Marketplaces • 1001-5000 employees • Est. 2019
Tech stack of Whatnot
Backed by Sequoia Capital, Y Combinator, Andreessen Horowitz

Join Whatnot, the largest live shopping platform in North America and Europe. As a Staff Software Engineer, you will lead technical work across the Discovery Platform and product-facing retrieval and recommendations. You will design and evolve search and serving infrastructure, build retrieval systems, and partner with ranking and machine learning engineers. This is a high-impact, hands-on role for someone who thrives in ambiguous spaces and is passionate about improving the buyer experience.

Missions

  • Design and evolve search and serving infrastructure that performs reliably through live shopping traffic spikes, including our largest sales events.
  • Build retrieval systems that let feed and search teams add candidate sources safely and quickly.
  • Partner with ranking and machine learning engineers to bring models and features into production with the latency and freshness they need.
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